Do affluent neighbourhoods pay more for transit access? Exploring the capitalization of employment accessibility within different housing submarkets in Vancouver
Bibliographic record
Abstract
Decades of research indicate that accessibility plays a fundamental role in the urban systems of cities by influencing land markets and household location choices. Accessibility is also often positioned as a policy tool in enhancing the well-being of disadvantaged population groups. Considered together, recent research into transportation equity underscores the need to critically investigate the distribution of accessibility with their affordability impacts. To better understand this dynamic, this research assesses variability in the relationship between employment access and house prices within different neighbourhood types in Metro Vancouver. We first calculate network accessibility to employment opportunities. Next, to integrate equity, we use sociodemographic indicators from census data to establish a typology of four neighbourhoods: Affluent, Stable Middle-class, At-Risk Middle-class and Economically-disadvantaged . Finally, using real estate data, we employ spatial econometric models to estimate differences in the capitalization of accessibility in residential property prices across these neighbourhood types. Results suggest that the value of transit access does differ by neighbourhood type in Metro Vancouver. While more affluent neighbourhoods exhibit the highest marginal willingness to pay for accessibility, predicted prices are highest in disadvantaged neighbourhoods due to their higher absolute levels of access. The research offers new insight into how property price effects vary according to different accessibility and sociodemographic contexts, and highlights important implications for both policy and future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".